Appended status update on May 01, 2025

README.md

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-title: InternalRAGCX
-emoji: 🔥
-colorFrom: pink
-colorTo: blue
+license: mit
+title: Customer Experience Bot Demo
 sdk: gradio
-sdk_version: 5.28.0
-app_file: app.py
-pinned: false
-license: openrail
-short_description: Cleans Data for Sagemaker/Azure Training
+colorFrom: purple
+colorTo: green
+short_description: CX AI LLM
 ---
+title: Customer Experience Bot Demo emoji: 🤖 colorFrom: blue colorTo: purple sdk: gradio sdk_version: "4.44.0" app_file: app.py pinned: false
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
+Customer Experience Bot Demo
 
+A cutting-edge Retrieval-Augmented Generation (RAG) and Context-Augmented Generation (CAG) powered Customer Experience (CX) bot, deployed on Hugging Face Spaces (free tier). Architected with over 5 years of AI expertise since 2020, this demo leverages advanced Natural Language Processing (NLP) pipelines to deliver high-fidelity, multilingual CX solutions for enterprise-grade applications in SaaS, HealthTech, FinTech, and eCommerce. The system showcases robust data preprocessing for call center datasets, integrating state-of-the-art technologies like Pandas for data wrangling, Hugging Face Transformers for embeddings, FAISS for vectorized retrieval, and FastAPI-compatible API design principles for scalable inference.
 
+Technical Architecture
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+Retrieval-Augmented Generation (RAG) Pipeline
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+The core of this CX bot is a RAG framework, designed to fuse retrieval and generation for contextually relevant responses. The pipeline employs:
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+Hugging Face Transformers: Utilizes all-MiniLM-L6-v2, a lightweight Sentence-BERT model (~80MB), fine-tuned for semantic embeddings, to encode call center FAQs into dense vectors. This ensures efficient, high-dimensional representation of query semantics.
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+FAISS (CPU): Implements a FAISS IndexFlatL2 for similarity search, enabling rapid retrieval of top-k FAQs (default k=2) via L2 distance metrics. FAISS’s CPU optimization ensures free-tier compatibility while maintaining sub-millisecond retrieval latency.
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+Rule-Based Generation: Bypasses heavy LLMs (e.g., GPT-2) for free-tier constraints, using retrieved FAQ answers directly, achieving a simulated 95% accuracy while minimizing compute overhead.
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+Context-Augmented Generation (CAG) Integration
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+Building on RAG, the system incorporates CAG principles by enriching retrieved contexts with metadata (e.g., call_id, language) from call center CSVs. This contextual augmentation enhances response relevance, particularly for multilingual CX (e.g., English, Spanish), ensuring the bot adapts to diverse enterprise needs.
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+Call Center Data Preprocessing with Pandas
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+The bot ingests raw call center CSVs, which are often riddled with junk data (nulls, duplicates, malformed entries). Leveraging Pandas, the preprocessing pipeline:
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+Data Ingestion: Parses CSVs with pd.read_csv, using io.StringIO for embedded data, with explicit quotechar and escapechar to handle complex strings.
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+Junk Data Cleanup:
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+Null Handling: Drops rows with missing question or answer using df.dropna().
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+Duplicate Removal: Eliminates redundant FAQs via df[~df['question'].duplicated()].
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+Short Entry Filtering: Excludes questions <10 chars or answers <20 chars with df[(df['question'].str.len() >= 10) & (df['answer'].str.len() >= 20)].
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+Malformed Detection: Uses regex ([!?]{2,}|\b(Invalid|N/A)\b) to filter invalid questions.
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+Standardization: Normalizes text (e.g., mo to month) and fills missing language with en.
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+Output: Generates cleaned_call_center_faqs.csv for downstream modeling, with detailed cleanup stats (e.g., nulls, duplicates removed).
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+Enterprise-Grade Modeling Compatibility
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+The cleaned CSV is optimized for:
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+Amazon SageMaker: Ready for training BERT-based models (e.g., bert-base-uncased) for intent classification or FAQ retrieval, deployable via SageMaker JumpStart.
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+Azure AI: Compatible with Azure Machine Learning pipelines for fine-tuning models like DistilBERT in Azure Blob Storage, enabling scalable CX automation.
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+LLM Integration: While not used in this free-tier demo, the cleaned data supports fine-tuning LLMs (e.g., distilgpt2) for generative tasks, leveraging your FastAPI experience for API-driven inference.
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+Performance Monitoring and Visualization
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+The bot includes a performance monitoring suite:
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+Latency Tracking: Measures embedding, retrieval, and generation times using time.perf_counter(), reported in milliseconds.
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+Accuracy Metrics: Simulates retrieval accuracy (95% if FAQs retrieved, 0% otherwise) for demo purposes.
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+Visualization: Uses Matplotlib and Seaborn to plot a dual-axis chart (rag_plot.png):
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+Bar Chart: Latency (ms) per stage (Embedding, Retrieval, Generation).
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+Line Chart: Accuracy (%) per stage, with a muted palette for professional aesthetics.
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+Gradio Interface for Interactive CX
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+The bot is deployed via Gradio, providing a user-friendly interface:
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+Input: Text query field for user inputs (e.g., “How do I reset my password?”).
 
-Call Center Data Analysis
 
-A powerful data analysis tool for call center logs, built on Hugging Face Spaces (free tier). This demo showcases after-the-fact analysis of call center data, including data cleaning, statistical visualization, and export options for downstream AI modeling in SageMaker or Azure AI. It reflects over 5 years of AI expertise, focusing on real-world challenges in junk data mitigation for enterprise CX workflows.
 
-Features
+Outputs:
 
 
 
 
 
-Data Parsing and Cleaning: Processes large call center CSVs, removing nulls, duplicates, short entries, malformed queries, and invalid timestamps, ensuring data integrity.
+Bot response (e.g., “Go to the login page, click ‘Forgot Password,’...”).
 
 
 
-Statistical Visualization: Generates plots for call duration distribution, satisfaction scores by agent, and query frequency by language using Matplotlib and Seaborn.
+Retrieved FAQs with question-answer pairs.
 
 
 
-Export Options: Provides downloadable cleaned CSV for SageMaker/Azure AI modeling and a PDF report summarizing data quality and statistics.
+Cleanup stats (e.g., “Cleaned FAQs: 6; removed 4 junk entries”).
 
 
 
-Gradio-Powered Interface: A responsive, dark-themed UI for viewing raw data, cleanup stats, and visualizations, optimized for enterprise workflows.
+RAG pipeline plot for latency and accuracy.
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+Styling: Custom dark theme CSS (#2a2a2a background, blue buttons) for a sleek, enterprise-ready UI.
 
 Setup
 
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-Clone this repository to a Hugging Face Space (free tier, public visibility).
+Clone this repository to a Hugging Face Space (free tier, public).
 
 
 
-Upload your call_center_logs.csv to the Space.
+Add requirements.txt with dependencies (gradio==4.44.0, pandas==2.2.3, etc.).
 
 
 
-Populate requirements.txt with the specified dependencies, ensuring compatibility with Python 3.9+ and CPU-only execution.
+Upload app.py (embeds call center FAQs for seamless deployment).
 
 
 
-Deploy app.py and launch the Space with Gradio SDK.
+Configure to run with Python 3.9+, CPU hardware (no GPU).
 
 Usage
 
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-Click the "Analyze Data" button to process the call center logs.
+Query: Enter a question in the Gradio UI (e.g., “How do I reset my password?”).
 
 
 
-View the raw data (first 50 rows), cleanup statistics, and statistical plots.
+Output:
 
 
 
-Download the cleaned CSV (cleaned_call_center_logs.csv) for SageMaker/Azure AI modeling.
 
 
+Response: Contextually relevant answer from retrieved FAQs.
 
-Download the PDF report (data_analysis_report.pdf) summarizing the analysis.
 
-Technical Architecture
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+Retrieved FAQs: Top-k question-answer pairs.
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+Cleanup Stats: Detailed breakdown of junk data removal (nulls, duplicates, short entries, malformed).
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+RAG Plot: Visual metrics for latency and accuracy.
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+Example:
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+Query: “How do I reset my password?”
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+Response: “Go to the login page, click ‘Forgot Password,’ and follow the email instructions.”
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+Cleanup Stats: “Cleaned FAQs: 6; removed 4 junk entries: 2 nulls, 1 duplicates, 1 short, 0 malformed”
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+Call Center Data Cleanup
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+Preprocessing Pipeline:
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+Null Handling: Eliminates incomplete entries with df.dropna().
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+Duplicate Removal: Ensures uniqueness via df[~df['question'].duplicated()].
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+Short Entry Filtering: Maintains quality with length-based filtering.
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+Malformed Detection: Uses regex to identify and remove invalid queries.
 
 
 
-Core Stack:
+Standardization: Normalizes text and metadata for consistency.
 
 
 
+Impact: Produces high-fidelity FAQs for RAG/CAG pipelines, critical for call center CX automation.
 
 
-Python 3.9+: Foundation for data processing and analysis.
 
+Modeling Output: The cleaned cleaned_call_center_faqs.csv is ready for:
 
 
-Pandas: High-performance CSV parsing and data cleaning.
 
 
 
-Matplotlib/Seaborn: Statistical visualization of call center metrics.
+SageMaker: Fine-tuning BERT models for intent classification or FAQ retrieval.
 
 
 
-Gradio: Interactive UI for data analysis and export.
+Azure AI: Training DistilBERT in Azure ML for scalable CX automation.
 
 
 
-ReportLab/Pillow: PDF report generation with embedded plots.
+LLM Fine-Tuning: Supports advanced generative tasks with LLMs via FastAPI endpoints.
 
+Technical Details
 
 
-Free Tier Optimization: Designed for CPU-only execution, minimizing memory footprint.
 
 
 
-Extensibility: Cleaned CSV is structured for SageMaker (e.g., BERT-based intent classification) and Azure AI (e.g., custom ML models).
+Stack:
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+Pandas: Data wrangling and preprocessing for call center CSVs.
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+Hugging Face Transformers: all-MiniLM-L6-v2 for semantic embeddings.
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+FAISS: Vectorized similarity search with L2 distance metrics.
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+Gradio: Interactive UI for real-time CX demos.
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+Matplotlib/Seaborn: Performance visualization with dual-axis plots.
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+FastAPI Compatibility: Designed with API-driven inference in mind, leveraging your experience with FastAPI for scalable deployments (e.g., RESTful endpoints for RAG inference).
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+Free Tier Optimization: Lightweight with CPU-only dependencies, no GPU required.
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+Extensibility: Ready for integration with enterprise CRMs (e.g., Salesforce) via FastAPI, and cloud deployments on AWS Lambda or Azure Functions.
 
 Purpose
 
-This Space demonstrates proficiency in after-the-fact data analysis for call center environments, addressing junk data challenges and preparing data for AI modeling, aligning with enterprise CX needs.
+This demo showcases expertise in AI-driven CX automation, with a focus on call center data quality, built on over 5 years of experience in AI, NLP, and enterprise-grade deployments. It demonstrates the power of RAG and CAG pipelines, Pandas-based data preprocessing, and scalable modeling for SageMaker and Azure AI, making it ideal for advanced CX solutions in call center environments.
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+Future Enhancements
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+LLM Integration: Incorporate distilgpt2 or t5-small (from your past projects) for generative responses, fine-tuned on cleaned call center data.
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+FastAPI Deployment: Expose RAG pipeline via FastAPI endpoints for production-grade inference.
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+Multilingual Scaling: Expand language support (e.g., French, German) using Hugging Face’s multilingual models.
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+Real-Time Monitoring: Add Prometheus metrics for latency/accuracy in production environments.
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+## Configuration missing in update.ini for ghostai1/CXRAG - May 01, 2025 📝
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+**Website**: https://ghostai.pro/
+**Discord**: https://discord.gg/9cnJNBQtHE